In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices’ offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network’s communication rate and IoT networks’ total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices’ available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices’ scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

In this paper, we focus on a cross-layer resource allocation problem for an opportunistic cognitive radio network, where secondary users (SUs) share a primary network’s licensed spectrum only when the primary network is sensed to be idle. We consider cooperative spectrum sensing, where SUs participate in sensing only when a benefit from resource allocation is guaranteed. Number of SUs for cooperative spectrum sensing is an important parameter as it defines both sensing and resource allocation performance. The fusion centre captures interaction between physical layer spectrum sensing, channel condition, and higher layer data buffer while selecting SUs for cooperative sensing. We consider heterogeneous data types at SUs’ data buffers during SU selection to make our system model more general. We form a mixed integer non-linear optimization problem, from which we select SUs’ set for sensing and resource allocation, appropriate sensing thresholds, and corresponding resource allocation parameters. Due to it’s combinatorial nature, the optimization problem is non-trivial. We devise an optimal algorithm to solve the optimization problem. Moreover, we also propose a computationally efficient and near-optimal greedy algorithm with suitable performance bounds. We also show advantage of our proposed optimal algorithms compared to other traditional methods through extensive simulations.

In this paper, we consider a system model in conjunction with two major technologies in 5G communications, i.e., mobile edge computing and spectrum sharing. An IoT network, which does not have access to any licensed spectrum, carries its computational task offloading activities with help of spectrum sharing. The IoT network cooperates with a licensed spectrum holding network by relaying its data and in return gets access to the licensed spectrum. The licensed spectrum holding network focuses on throughput maximization, whereas, the IoT network tries to maximize task computation rate. We formulate an optimization problem giving importance to both networks’ interests. Typical IoT nodes may be energy-constrained, which is considered along with task computation time constraints. Parallel computation is considered while computing IoT nodes’ computational tasks at the mobile edge computing server, where the processor is allocated based on IoT nodes’ offloading capabilities. The advantage of such processor allocation is shown in the result section. Moreover, we also show how both IoT and licensed spectrum holding networks benefit from the spectrum sharing by caring for each other requirements, which echoes the fact, i.e., Sharing is Caring.

In this paper, we consider an opportunistic cognitive radio (CR) network comprising of a primary base station (PBS) and a secondary base station (SBS), both associated with multiple users. SBS obtains the right to use the licensed spectrum of PBS by paying a remuneration. Moreover, both PBS and SBS provide service to their corresponding users in exchange of an economic price. PBS, being a profit maximizer, optimally balances between the revenue directly coming from its own users and the monetary reward provided by SBS. On the other hand, SBS must maintain a positive gain to support its infrastructure. Thus, a two-person non-cooperative bargaining game is formulated where both PBS and SBS concur at the values of two parameters, i.e., the activity factor of PBS and the payment made by SBS for opportunistically accessing the licensed spectrum. PBS has knowledge of detection and false-alarm probabilities of the spectrum sensing by SBS and hence can infer how the interference created by missed detection can degrade its achievable capacity. In this framework, we utilize Nash solution to determine a mutually beneficial outcome of the bargaining process. The solution leads to a mathematical relation dictating the viability of the network in the long-run. This result holds for arbitrary continuous user demand functions. For linear user demand, we obtain closed-form expression of the bargaining solution that enables us to analyze the influence of different key system parameters on the economic outcomes.

In this paper, we consider a cooperative cognitive radio network where the fusion center acts as final decision maker as well as resource allocator. The secondary users access the licensed band only when the primary user is inactive. The fusion center behaves as a profit seeking entity, which charges the secondary users for letting them use the licensed band. However, the fusion center pays a penalty to the primary network for causing interference. Hence to improve detection performance, the fusion center encourages the SUs to perform cooperative spectrum sensing. Moreover, we map the secondary users' average data rate with a payoff. All the secondary users maintain data buffers and pay to the fusion center according to their traffic type. The fusion center tries to maximize it's own utility while maintaining non-negative utilities at the secondary users. Feasible conditions which guarantee non-negative utilities at secondary users are evaluated. We show that the optimal number of observations which is required for spectrum sensing at secondary users strongly depends on the interference penalty paid by the fusion center. The secondary users get time durations for accessing the licensed band according to their stored energies and paid costs to the fusion center.

In this letter, we investigate distributed detection over channels with memory. We model the wireless channel between each sensor and the fusion center by a two state Markov chain and derive the optimal fusion rule. We demonstrate the improved detection performance of the derived fusion rule for burst error channels, over a channel aware fusion rule for a memoryless channel and the Chair-Varshney fusion rule. We further show that when the number of sensor decisions is large, as well as when the channel is memoryless, the derived fusion rule reduces to the well known counting rule.